First Load Libraries

library(readxl)
## Warning: package 'readxl' was built under R version 4.3.3
library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)
library(plotly)
## Warning: package 'plotly' was built under R version 4.3.1
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
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##     filter
## The following object is masked from 'package:graphics':
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##     layout
library(ggplotify)
## Warning: package 'ggplotify' was built under R version 4.3.2
library(rempsyc)
## Warning: package 'rempsyc' was built under R version 4.3.3
## Suggested APA citation: Thériault, R. (2023). rempsyc: Convenience functions for psychology. 
## Journal of Open Source Software, 8(87), 5466. https://doi.org/10.21105/joss.05466
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
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##     date, intersect, setdiff, union
library(cowplot)
## Warning: package 'cowplot' was built under R version 4.3.1
## 
## Attaching package: 'cowplot'
## The following object is masked from 'package:lubridate':
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##     stamp

Loading dataset

final_df = read_excel("Z:\\30_Oscar_Ortiz\\MENTOR\\AL\\final_analysis\\FINAL_ANALYSIS\\final_merged_dataset.xlsx") # change for your use

Lets plot how people did before and after the sleep journaling to see if there is consistency between their responses

p= ggplot(final_df, aes(x=success_rate, y=success_rate_2)) + 
  geom_point()+
  geom_smooth(method=lm, se = FALSE)+
  labs(y= "nback_success_rate_1", x = "nback_success_rate_1") 

ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
p = ggplot(final_df, aes(x=percent_match_ordered_mean, y=percent_match_ordered_mean_2)) + 
  geom_point()+
  geom_smooth(method=lm, se = FALSE)+
  labs(y= "percent_match_ordered_mean_1", x = "percent_match_ordered_mean_2") 

ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
p = ggplot(final_df, aes(x=matching_words_count_mean, y=matching_words_count_mean_2)) + 
  geom_point()+
  geom_smooth(method=lm, se = FALSE)+
  labs(y= "matching_words_count_mean", x = "matching_words_count_mean_2") 

ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'

Looks like there is good reliability in the post test.

Plotting performance before and after

df = as.data.frame(final_df |>
  select(contains(c("subject","success")))) |>
  pivot_longer(cols = c(success_rate, success_rate_2), names_to = "Timepoint", values_to = "Value") |>
  mutate(Timepoint =  case_when( # change the y and n responses to 1 and zero
      Timepoint == "success_rate" ~ "Pre",
      Timepoint == "success_rate_2" ~ "Post",
      .default ="text")) |>
  mutate(Timepoint = factor(Timepoint,levels=c("Pre","Post")))|>
  group_by(Timepoint)|>
  mutate(paired = seq(1:length(Timepoint)))


p = ggplot(df, aes(x = Timepoint)) +
 geom_line(aes(group = paired,y =Value),
            linewidth=1,alpha = 0.7, 
            position = position_jitter(width = 0.1, seed = 3922)) +
  geom_point(aes(color=Timepoint,y=Value),size = 6,position = position_jitter(width = 0.1, seed = 3922)) +
  labs(y= "% of Correct Letters", x = "Pre or Post Sleep Journaling")+
    theme(
    axis.text = element_text(size = 14),       # Adjust axis text size
    axis.title = element_text(size = 16),      # Adjust axis title size
    plot.title = element_text(size = 18),      # Adjust plot title size
    legend.text = element_text(size = 12)      # Adjust legend text size
  )
ggplotly(p)
df = as.data.frame(final_df |>
  select(contains(c("subject","percent_match_ordered_mean")))) |>
  pivot_longer(cols = c(percent_match_ordered_mean, percent_match_ordered_mean_2), names_to = "Timepoint", values_to = "Value") |>
  mutate(Timepoint =  case_when( # change the y and n responses to 1 and zero
      Timepoint == "percent_match_ordered_mean" ~ "Pre",
      Timepoint == "percent_match_ordered_mean_2" ~ "Post",
      .default ="text")) |>
  mutate(Timepoint = factor(Timepoint,levels=c("Pre","Post")))|>
  group_by(Timepoint)|>
  mutate(paired = seq(1:length(Timepoint)))


p = ggplot(df, aes(x = Timepoint)) +
 geom_line(aes(group = paired,y =Value),
            linewidth=1,alpha = 0.7, 
            position = position_jitter(width = 0.1, seed = 3922)) +
  geom_point(aes(color=Timepoint,y=Value),size = 6,position = position_jitter(width = 0.1, seed = 3922)) +
  labs(y= "% of Correct Numbers", x = "Pre or Post Sleep Journaling")+
    theme(
    axis.text = element_text(size = 14),       # Adjust axis text size
    axis.title = element_text(size = 16),      # Adjust axis title size
    plot.title = element_text(size = 18),      # Adjust plot title size
    legend.text = element_text(size = 12)      # Adjust legend text size
  )
ggplotly(p)
df = as.data.frame(final_df |>
  select(contains(c("subject","matching_words_count_mean")))) |>
  pivot_longer(cols = c(matching_words_count_mean, matching_words_count_mean_2), names_to = "Timepoint", values_to = "Value") |>
  mutate(Timepoint =  case_when( # change the y and n responses to 1 and zero
      Timepoint == "matching_words_count_mean" ~ "Pre",
      Timepoint == "matching_words_count_mean_2" ~ "Post",
      .default ="text")) |>
  mutate(Timepoint = factor(Timepoint,levels=c("Pre","Post")))|>
  group_by(Timepoint)|>
  mutate(paired = seq(1:length(Timepoint)))



p = ggplot(df, aes(x = Timepoint)) +
 geom_line(aes(group = paired,y =Value),
            linewidth=1,alpha = 0.7, 
            position = position_jitter(width = 0.1, seed = 3922)) +
  geom_point(aes(color=Timepoint,y=Value),size = 6,position = position_jitter(width = 0.1, seed = 3922)) +
  labs(y= " Words Remebered", x = "Pre or Post Sleep Journaling")+
    theme(
    axis.text = element_text(size = 14),       # Adjust axis text size
    axis.title = element_text(size = 16),      # Adjust axis title size
    plot.title = element_text(size = 18),      # Adjust plot title size
    legend.text = element_text(size = 12)      # Adjust legend text size
  )

ggplotly(p)

Question: Does the amount of hours spent sleeping affect memory consolidation?

merged_dataset = read_excel("Z:\\30_Oscar_Ortiz\\MENTOR\\AL\\final_analysis\\\\FINAL_ANALYSIS\\final_merged_dataset.xlsx")
p = ggplot(merged_dataset, aes(x=hours_sleeping_mean, y=success_rate_2)) + 
  geom_point(size = 6)+
  geom_smooth(method=lm, se = FALSE,size = 2)  +
  labs(y= "% of Correct Letters", x = "Hours Sleeping")+
    theme(
    axis.text = element_text(size = 14),       # Adjust axis text size
    axis.title = element_text(size = 16),      # Adjust axis title size
    plot.title = element_text(size = 18),      # Adjust plot title size
    legend.text = element_text(size = 12)      # Adjust legend text size
  )
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
# super positive relationship




p = ggplot(merged_dataset, aes(x=hours_sleeping_mean, y=percent_match_ordered_mean)) + 
  geom_point(size = 6)+
  geom_smooth(method=lm, se = FALSE,size = 2)  +
  labs(y= "% of Correct Numbers ", x = "Hours Sleeping")+
    theme(
    axis.text = element_text(size = 14),       # Adjust axis text size
    axis.title = element_text(size = 16),      # Adjust axis title size
    plot.title = element_text(size = 18),      # Adjust plot title size
    legend.text = element_text(size = 12)      # Adjust legend text size
  )
ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
# super negative relationship


p = ggplot(merged_dataset, aes(x=hours_sleeping_mean, y=matching_words_count_mean)) + 
  geom_point(size = 6)+
  geom_smooth(method=lm, se = FALSE,size = 2)  +
  labs(y= "Words Remembered ", x = "Hours Sleeping")+
    theme(
    axis.text = element_text(size = 14),       # Adjust axis text size
    axis.title = element_text(size = 16),      # Adjust axis title size
    plot.title = element_text(size = 18),      # Adjust plot title size
    legend.text = element_text(size = 12)      # Adjust legend text size
  )
ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
# Create subject IDs
subject_id <- sprintf("%02d", 1:7)

# Create random values for pre and post timepoints
pre_values <- c(6,5,10,8,9,7,5)
post_values <- c(10,5,9,7,8,7,7)

# Create dataframe
df <- data.frame(subject_id = subject_id,
                 Pre = pre_values,
                 Post = post_values)



df = df |> 
  pivot_longer(cols = c(Pre, Post), names_to = "Timepoint", values_to = "Value") |>
  mutate(Timepoint = factor(Timepoint,levels=c("Pre","Post")))|>
  group_by(Timepoint)|>
  mutate(paired = seq(1:length(Timepoint)))






p = ggplot(df, aes(x = Timepoint)) +
 geom_line(aes(group = paired,y =Value),
            linewidth=1,alpha = 0.7, 
            position = position_jitter(width = 0.1, seed = 3922)) +
  geom_point(aes(color=Timepoint,y=Value),size = 6,position = position_jitter(width = 0.1, seed = 3922)) +
  labs(y= "GLOBAL PSQI Score", x = "Pre or Post Sleep Journaling")+
    theme(
    axis.text = element_text(size = 14),       # Adjust axis text size
    axis.title = element_text(size = 16),      # Adjust axis title size
    plot.title = element_text(size = 18),      # Adjust plot title size
    legend.text = element_text(size = 12)      # Adjust legend text size
  )
ggplotly(p)

Now to look into the sleeping behaviour

data<- read_excel("Z:\\30_Oscar_Ortiz\\MENTOR\\AL\\final_analysis\\sleep_journal.xlsx")

cleaning and formatting the data

time_bed <- as.POSIXct(data$time_bed)
time_up <- as.POSIXct(data$time_up)

# Converts time_bed and time_up to a POSIXct object 

data$time_24_hour_bed <- format(time_bed, "%H:%M:%S")
data$time_24_hour_up <- format(time_up, "%H:%M:%S")

# Converts POSIXct object time_bed and time_up to a character string
# Note: POSIXct = calendar time, POSIXlt = local time

data$time_12_hour_bed <- format(strptime(data$time_24_hour_bed, "%H:%M:%S"), "%I:%M %p")
data$time_12_hour_up <- format(strptime(data$time_24_hour_up, "%H:%M:%S"), "%I:%M %p")

# Converts time_bed and time_up string to POSIXlt objects
# POSIXlt objects are then formatted with AM/PM by a string

data$numeric_time_bed <- hour(time_bed) + minute(time_bed)/60 + second(time_bed)/3600
data$numeric_time_up <- hour(time_up) + minute(time_up)/60 + second(time_up)/3600

# Lubridate functions extracts time_bed and time_up data
# Data is converted into a numerical value

data$hours_spent_bed <- difftime(time_up, time_bed, units = "hours")

# Calculates difference in hours between time_up and time_bed

if (as.numeric(data$hours_spent_bed[1]) < 0) {
  data$hours_spent_bed <- data$hours_spent_bed + 24  
}

# 1. Checks if numerical hours_spent_bed is negative
# 2. If true, adds 24 hours to values in hours_spent_bed 


data$hours_spent_bed <- ifelse(as.numeric(data$hours_spent_bed) > 23, 
                               data$hours_spent_bed - 24, 
                               data$hours_spent_bed)

earliest_dates <- data|>
  group_by(id)|>
  summarise(earliest_date = min(date))

# 1. Assigns data set to variable and feeds result down
# 2. Groups data by ID column for ID operations later on
# 3. Creates subset containing earliest_date for each ID


earliest_dates$earliest_date = as.Date(earliest_dates$earliest_date)
data$date = as.Date(data$date)

# 1. Formats variable earliest_date in earliest_dates
# 2. Formats variable date in data

data_clean = data|>
  group_by(id)|>
  mutate(earliest_date = min(date)) |>
  ungroup()|>
  mutate(days_from_start = as.numeric(date - as.Date(earliest_date)))


# cannot get time to go to bed yet so we move.

data_clean <- data_clean %>%
  mutate(subject_id = case_when(
    id == "AL" ~ "S01",
    id == "OOA" ~ "S02",
    id == "EL" ~ "S03",
    id == "mjnt" ~ "S04",
    id == "IT" ~ "S05",
    id == "pt" ~ "S06",
    id == "VM" ~ "S07",
    TRUE ~ as.character(id)
  ))

ChatGPT Time Conversion Formula

convert_to_12_hour <- function(numeric_time) {
  hours <- floor(numeric_time)
  minutes <- round((numeric_time - hours) * 60)
  meridiem <- ifelse(hours < 12, "AM", "PM")
  hours <- ifelse(hours == 0, 12, ifelse(hours <= 12, hours, hours - 12))
  paste0(hours, ":", sprintf("%02d", minutes), " ", meridiem)
}

# 1. Defines a function called numeric_time
# 2. Extracts integer (hour) part of numeric_time
# 3. Calculates minutes using extracted hour data
# 4. Determines if time is AM or PM using vector conditional
# 5. Converts hours to a 12-hour format, if:
# hours = 0, hours = 12
# hours <= 12, hours = hours
# hours > 12, hours = hours - 12
# 6. Makes a string in 12-hour format

data_clean <- data_clean |>
  mutate(formatted_time_12_hour_up = convert_to_12_hour(numeric_time_up))

# 2. Creates a new column with values equal to a function

print(data_clean$formatted_time_12_hour_up)
##   [1] "9:50 AM"  "8:45 AM"  "8:35 AM"  "11:00 AM" "7:20 AM"  "7:15 AM" 
##   [7] "8:00 AM"  "7:30 AM"  "9:17 AM"  "9:25 AM"  "7:12 AM"  "7:12 AM" 
##  [13] "7:15 AM"  "7:27 AM"  "7:07 AM"  "5:30 AM"  "7:57 AM"  "7:20 AM" 
##  [19] "7:15 AM"  "7:11 AM"  "7:05 AM"  "7:08 AM"  "7:52 AM"  "9:15 AM" 
##  [25] "8:25 AM"  "8:55 AM"  "9:23 AM"  "9:39 AM"  "9:55 AM"  "9:30 AM" 
##  [31] "9:30 AM"  "7:12 AM"  "8:20 AM"  "7:40 AM"  "7:35 AM"  "8:00 AM" 
##  [37] "10:00 AM" "9:55 AM"  "5:30 AM"  "6:45 AM"  "5:30 AM"  "5:48 AM" 
##  [43] "6:30 AM"  "8:00 AM"  "6:30 AM"  "7:30 AM"  "7:30 AM"  "8:00 AM" 
##  [49] "8:45 AM"  "7:15 AM"  "6:30 AM"  "6:30 AM"  "6:30 AM"  "7:30 AM" 
##  [55] "6:00 AM"  "6:30 AM"  "6:30 AM"  "6:30 AM"  "6:30 AM"  "6:30 AM" 
##  [61] "7:30 AM"  "8:30 AM"  "8:30 AM"  "8:30 AM"  "9:40 AM"  "8:30 AM" 
##  [67] "7:00 AM"  "7:00 AM"  "3:30 AM"  "3:50 AM"  "3:30 AM"  "4:00 AM" 
##  [73] "4:15 AM"  "4:00 AM"  "4:00 AM"  "4:00 AM"  "4:00 AM"  "4:00 AM" 
##  [79] "4:00 AM"  "4:00 AM"  "4:00 AM"  "3:45 AM"  "4:30 AM"  "4:00 AM" 
##  [85] "4:00 AM"  "3:40 AM"  "3:30 AM"  "4:00 AM"  "3:45 AM"  "3:30 AM" 
##  [91] "3:45 AM"  "3:45 AM"  "3:45 AM"  "3:45 AM"  "6:00 AM"  "4:00 AM" 
##  [97] "3:45 AM"  "4:00 AM"  "4:00 AM"  "6:45 AM"  "6:20 AM"  "5:00 AM" 
## [103] "6:30 AM"  "6:45 AM"  "6:10 AM"  "6:30 AM"  "6:30 AM"  "6:40 AM" 
## [109] "6:00 AM"  "7:08 AM"  "8:00 AM"  "8:00 AM"  "8:00 AM"  "8:00 AM" 
## [115] "8:30 AM"  "7:00 AM"  "6:45 AM"  "6:45 AM"  "7:00 AM"  "6:30 AM" 
## [121] "7:32 AM"  "7:00 AM"  "6:37 AM"  "6:45 AM"  "6:32 AM"  "8:00 AM" 
## [127] "9:00 AM"  "6:00 AM"  "7:00 AM"  "6:45 AM"  "8:38 AM"  "7:45 AM" 
## [133] "8:45 AM"  "6:15 AM"  "7:45 AM"  "1:00 AM"  "9:30 AM"  "8:47 AM" 
## [139] "7:52 AM"  "7:15 AM"  "7:15 AM"  "7:30 AM"  "7:30 AM"  "7:45 AM" 
## [145] "4:50 AM"  "5:50 AM"  "9:00 AM"  "10:30 AM" "7:50 AM"  "5:00 AM" 
## [151] "2:00 AM"  "2:00 AM"  "2:00 AM"  "2:00 AM"  "2:30 AM"  "3:00 AM" 
## [157] "1:45 AM"  "2:00 AM"  "2:00 AM"  "2:00 AM"  "2:00 AM"  "2:00 AM" 
## [163] "3:00 AM"  "1:30 AM"  "2:00 AM"  "2:00 AM"  "2:00 AM"  "2:00 AM" 
## [169] "4:00 AM"  "4:00 AM"  "2:00 AM"  "2:00 AM"  "2:00 AM"  "2:00 AM" 
## [175] "4:00 AM"  "4:00 AM"  "2:10 AM"  "4:00 AM"  "3:45 AM"  "4:00 AM" 
## [181] "3:30 AM"  "8:30 AM"  "8:20 AM"  "8:00 AM"  "8:05 AM"  "8:00 AM" 
## [187] "9:00 AM"  "9:30 AM"  "8:00 AM"  "7:00 AM"  "6:30 AM"  "7:15 AM" 
## [193] "10:30 AM" "8:00 AM"  "11:00 AM" "7:45 AM"  "6:20 AM"  "8:00 AM" 
## [199] "9:00 AM"  "8:00 AM"  "9:00 AM"  "NA:NA NA" "7:30 AM"

Data Visualization - Hours in Bed Consistency

data_clean <- data_clean %>%
  mutate(subject_id = case_when(
    id == "OOA" ~ "S01",
    id == "AL" ~ "S02",
    id == "EL" ~ "S03",
    id == "mjnt" ~ "S04",
    id == "IT" ~ "S05",
    id == "pt" ~ "S06",
    id == "VM" ~ "S07",
    id == "NA" ~ "S01",
    TRUE ~ as.character(id)
  ))

data_clean <- data_clean[-202, ]
p = ggplot(data_clean, aes(x = days_from_start, y = hours_spent_bed, color = factor(subject_id))) + 
  geom_point() + 
  geom_line() +
  facet_wrap(~subject_id) +
  labs(x = "Days Since Start", y = "Hours Spent in bed") +
  scale_y_continuous(breaks = seq(0, 12, by = 4),  
                     labels = c("0", "4", "8", "12"),  
                     limits = c(0, 12))  

ggplotly(p)
# 1. States x and y-axis variables, color represents a unique ID
# 2. Adds points to the plot for each data point in data_clean 
# 3. Connects each point from left to right creating line graph
# 4. Creates distinct subplots using steps 1-3 for each ID
# 5. Labels x and y-axis

Data Visualization - Hours Bed vs. Hours Sleeping

scatter_plot <- ggplot(data_clean, aes(x = days_from_start, y = hours_spent_bed, color = factor(subject_id))) + 
  geom_point() + 
  geom_line() +
  facet_wrap(~subject_id) +
  labs(x = "Days Since Start", y = "Hours Spent in Bed/Sleeping") 

# Creates scatter plot based on hours spent in bed data


combined_plot <- scatter_plot +
  geom_line(aes(y = as.numeric(hours_sleeping), group = id), color = "black", size = 1.0) 

# ----------------------------------------------------------------------------------------- 13h bed value, find way to min to max, readable breaks
# scale_y_continuous(breaks = seq(0, 12, by = 4),  
# labels = c("0", "4", "8", "12"),  
# limits = c(0, 12))  
# -----------------------------------------------------------------------------------------



# Overlays a black line plot based on hours_sleeping on the scatter plot

ggplotly(combined_plot)

Data Visualization - Sleep Rating

p = ggplot(data_clean, aes(x = sleep_rating, fill = subject_id)) + 
  geom_histogram(bins = 10) +
  facet_wrap(~subject_id, nrow = 1) +
  labs(x = "Personal Sleep Rating", y = "Frequency") 

ggplotly(p)
# Specify the columns containing repeated measures
repeated_measure_columns <- c("subject_id","minutes2asleep", "hours_sleeping", "sleep_rating", "anxiety_rating", "rested_rating", "remember_dream", "dream_vivid_rating", "dream_type", "sleep_interruption", "hours_napped", "numeric_time_bed", "numeric_time_up", "hours_spent_bed" )

sleep_dat_sub = data_clean |>
  select(all_of(repeated_measure_columns))# |>
 # na.omit()

run some basic correlations to see if more sleep = better rest

p = ggplot(sleep_dat_sub, aes(x=hours_sleeping, y=sleep_rating,color=subject_id)) + 
  geom_point()+
  geom_smooth(method=lm)+
  labs(y= "sleep rate", x = "hours of sleep") 
ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
# clear positive relationship


p = ggplot(sleep_dat_sub, aes(x=hours_sleeping, y=anxiety_rating,color=subject_id)) + 
  geom_point()+
  geom_smooth(method=lm)+
  labs(y= "anxiety rate", x = "hours of sleep") 
ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
# not super clear relationship actually


p = ggplot(sleep_dat_sub, aes(x=hours_sleeping, y=rested_rating,color=subject_id)) + 
  geom_point()+
  geom_smooth(method=lm)+
  labs(y= "resting rate", x = "hours of sleep") 
ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
# also a positive relantionship 



p = ggplot(sleep_dat_sub, aes(x=numeric_time_up, y=rested_rating,color=subject_id)) + 
  geom_point()+
  geom_smooth(method=lm)+
  labs(y= "resting rate", x = "Wake up time") 
ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
# not a clear relationship

Dreaming frequency plots

# Create the bar plot using ggplot
p = sleep_dat_sub |>
  select(remember_dream)|>
  na.omit()|>
  ggplot(aes(x = remember_dream,fill = remember_dream)) +
  geom_bar()
ggplotly(p)
# more nights people did not remember their dreams


p = sleep_dat_sub |>
  select(dream_vivid_rating,hours_sleeping,subject_id)|>
  na.omit()|>
  ggplot(aes(x=hours_sleeping, y=dream_vivid_rating,color=subject_id)) + 
  geom_point()+
  geom_smooth(method=lm,se = FALSE)+
  labs(y= "dream vivid rating", x = "hours of sleep") 
ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
# interesting... s05 did not remember their dreams at all. 


p = ggplot(sleep_dat_sub, aes(x=hours_sleeping, y=sleep_rating,color=remember_dream)) + 
  geom_point()+
  geom_smooth(method=lm)+
  labs(y= "sleep rate", x = "hours of sleep") 

ggplotly(p)
## `geom_smooth()` using formula = 'y ~ x'
#interesting... nights where the dream is not being remembered have a better relationship between sleep rate and hours of sleep